from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2020-12-16 14:07:34.750269
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'1. Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64('2020-12-06'),
'red', 'inside top left'),
'2. Soft Lockdown': (np.datetime64('2020-12-06'), np.datetime64(TODAY),
'orange', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Wed, 16, Dec, 2020
Time: 14:07:38
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -43.6501
Nobs: 142.000 HQIC: -44.7622
Log likelihood: 1508.76 FPE: 1.69951e-20
AIC: -45.5235 Det(Omega_mle): 9.21143e-21
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.458799 0.174483 2.629 0.009
L1.Burgenland 0.148193 0.084549 1.753 0.080
L1.Kärnten -0.235108 0.068253 -3.445 0.001
L1.Niederösterreich 0.102598 0.205314 0.500 0.617
L1.Oberösterreich 0.245622 0.169896 1.446 0.148
L1.Salzburg 0.175133 0.087449 2.003 0.045
L1.Steiermark 0.096299 0.122974 0.783 0.434
L1.Tirol 0.142619 0.080634 1.769 0.077
L1.Vorarlberg 0.010345 0.078360 0.132 0.895
L1.Wien -0.126377 0.165641 -0.763 0.445
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.579957 0.228165 2.542 0.011
L1.Burgenland 0.013776 0.110561 0.125 0.901
L1.Kärnten 0.363517 0.089252 4.073 0.000
L1.Niederösterreich 0.130737 0.268481 0.487 0.626
L1.Oberösterreich -0.216951 0.222166 -0.977 0.329
L1.Salzburg 0.189909 0.114354 1.661 0.097
L1.Steiermark 0.240973 0.160809 1.499 0.134
L1.Tirol 0.140622 0.105443 1.334 0.182
L1.Vorarlberg 0.190236 0.102469 1.857 0.063
L1.Wien -0.611721 0.216603 -2.824 0.005
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.306580 0.074658 4.106 0.000
L1.Burgenland 0.103683 0.036177 2.866 0.004
L1.Kärnten -0.023943 0.029204 -0.820 0.412
L1.Niederösterreich 0.103322 0.087850 1.176 0.240
L1.Oberösterreich 0.283538 0.072695 3.900 0.000
L1.Salzburg -0.005333 0.037418 -0.143 0.887
L1.Steiermark -0.034584 0.052618 -0.657 0.511
L1.Tirol 0.088801 0.034502 2.574 0.010
L1.Vorarlberg 0.130525 0.033529 3.893 0.000
L1.Wien 0.054849 0.070875 0.774 0.439
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.203959 0.086270 2.364 0.018
L1.Burgenland -0.004645 0.041803 -0.111 0.912
L1.Kärnten 0.021916 0.033746 0.649 0.516
L1.Niederösterreich 0.026894 0.101513 0.265 0.791
L1.Oberösterreich 0.400796 0.084002 4.771 0.000
L1.Salzburg 0.092200 0.043238 2.132 0.033
L1.Steiermark 0.195201 0.060802 3.210 0.001
L1.Tirol 0.029760 0.039868 0.746 0.455
L1.Vorarlberg 0.103430 0.038744 2.670 0.008
L1.Wien -0.069844 0.081898 -0.853 0.394
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.640594 0.183436 3.492 0.000
L1.Burgenland 0.079021 0.088887 0.889 0.374
L1.Kärnten 0.003246 0.071755 0.045 0.964
L1.Niederösterreich -0.083050 0.215849 -0.385 0.700
L1.Oberösterreich 0.128656 0.178614 0.720 0.471
L1.Salzburg 0.038291 0.091937 0.416 0.677
L1.Steiermark 0.126621 0.129284 0.979 0.327
L1.Tirol 0.217470 0.084772 2.565 0.010
L1.Vorarlberg 0.020959 0.082381 0.254 0.799
L1.Wien -0.151909 0.174141 -0.872 0.383
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.188562 0.127270 1.482 0.138
L1.Burgenland -0.035979 0.061671 -0.583 0.560
L1.Kärnten -0.012750 0.049785 -0.256 0.798
L1.Niederösterreich 0.179180 0.149758 1.196 0.232
L1.Oberösterreich 0.406618 0.123924 3.281 0.001
L1.Salzburg -0.026947 0.063787 -0.422 0.673
L1.Steiermark -0.046918 0.089699 -0.523 0.601
L1.Tirol 0.187519 0.058816 3.188 0.001
L1.Vorarlberg 0.032033 0.057157 0.560 0.575
L1.Wien 0.139625 0.120821 1.156 0.248
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.211536 0.160026 1.322 0.186
L1.Burgenland 0.085011 0.077543 1.096 0.273
L1.Kärnten -0.044909 0.062598 -0.717 0.473
L1.Niederösterreich -0.057686 0.188302 -0.306 0.759
L1.Oberösterreich -0.126932 0.155819 -0.815 0.415
L1.Salzburg 0.006046 0.080204 0.075 0.940
L1.Steiermark 0.400078 0.112785 3.547 0.000
L1.Tirol 0.517081 0.073953 6.992 0.000
L1.Vorarlberg 0.229939 0.071868 3.199 0.001
L1.Wien -0.217472 0.151917 -1.432 0.152
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.095368 0.185848 0.513 0.608
L1.Burgenland 0.031327 0.090056 0.348 0.728
L1.Kärnten -0.114883 0.072699 -1.580 0.114
L1.Niederösterreich 0.184862 0.218687 0.845 0.398
L1.Oberösterreich 0.019088 0.180962 0.105 0.916
L1.Salzburg 0.223975 0.093145 2.405 0.016
L1.Steiermark 0.150787 0.130984 1.151 0.250
L1.Tirol 0.087584 0.085887 1.020 0.308
L1.Vorarlberg 0.039435 0.083464 0.472 0.637
L1.Wien 0.293143 0.176431 1.662 0.097
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.592455 0.102627 5.773 0.000
L1.Burgenland -0.011421 0.049730 -0.230 0.818
L1.Kärnten -0.000581 0.040145 -0.014 0.988
L1.Niederösterreich -0.046450 0.120761 -0.385 0.701
L1.Oberösterreich 0.276458 0.099929 2.767 0.006
L1.Salzburg 0.002922 0.051436 0.057 0.955
L1.Steiermark 0.018777 0.072331 0.260 0.795
L1.Tirol 0.073555 0.047427 1.551 0.121
L1.Vorarlberg 0.186608 0.046090 4.049 0.000
L1.Wien -0.085886 0.097427 -0.882 0.378
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.129544 -0.014496 0.185638 0.238630 0.031862 0.078536 -0.127017 0.138867
Kärnten 0.129544 1.000000 -0.032180 0.171154 0.118770 -0.166015 0.164812 0.019764 0.290815
Niederösterreich -0.014496 -0.032180 1.000000 0.241113 0.059196 0.179762 0.092738 0.021340 0.362483
Oberösterreich 0.185638 0.171154 0.241113 1.000000 0.263926 0.271565 0.073045 0.048445 0.054629
Salzburg 0.238630 0.118770 0.059196 0.263926 1.000000 0.139904 0.054333 0.068925 -0.051852
Steiermark 0.031862 -0.166015 0.179762 0.271565 0.139904 1.000000 0.091375 0.061994 -0.175971
Tirol 0.078536 0.164812 0.092738 0.073045 0.054333 0.091375 1.000000 0.124736 0.103734
Vorarlberg -0.127017 0.019764 0.021340 0.048445 0.068925 0.061994 0.124736 1.000000 0.068038
Wien 0.138867 0.290815 0.362483 0.054629 -0.051852 -0.175971 0.103734 0.068038 1.000000